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output_dir = /home/ljw/sdc1/CRISPR_results | |
seed = 63036 | |
# device = cpu # cpu, cuda, if not specified, use cuda if available | |
log = WARNING | |
[dataset] | |
owner = ljw20180420 | |
data_name = SX_spcas9 # SX_spcas9, SX_spymac, SX_ispymac | |
test_ratio = 0.05 | |
validation_ratio = 0.05 | |
[data loader] | |
batch_size = 100 | |
[optimizer] | |
optimizer = adamw_torch # adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision, adafactor | |
learning_rate = 0.001 | |
[scheduler] | |
scheduler = linear # linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup, inverse_sqrt, reduce_lr_on_plateau, cosine_with_min_lr, warmup_stable_decay | |
num_epochs = 30.0 | |
warmup_ratio = 0.05 | |
[CRISPR transformer] | |
hidden_size = 256 # model embedding dimension | |
num_hidden_layers = 3 # number of EncoderLayer | |
num_attention_heads = 4 # number of attention heads | |
intermediate_size = 1024 # FeedForward intermediate dimension size | |
hidden_dropout_prob = 0.1 # The dropout probability for all fully connected layers in the embeddings, encoder, and pooler | |
attention_probs_dropout_prob = 0.1 # The dropout ratio for the attention probabilities | |
[CRISPR diffuser] | |
max_micro_homology = 7 | |
MCMC_corrector_factor = [0., 0., 1.] | |
unet_channels = [32, 64, 96, 64, 32] | |
noise_scheduler = exp # linear, cosine, exp, uniform | |
noise_timesteps = 20 | |
cosine_factor = 0.008 | |
exp_scale = 5.0 | |
exp_base = 5.0 | |
uniform_scale = 1.0 | |
display_scale_factor = 0.1 | |
[inDelphi] | |
DELLEN_LIMIT = 60 | |
[Lindel] | |
Lindel_dlen = 30 | |
Lindel_mh_len = 4 | |
Lindel_reg_const = 0.01 | |
Lindel_reg_mode = l2 | |
[FOREcasT] | |
FOREcasT_MAX_DEL_SIZE = 30 | |
FOREcasT_reg_const = 0.01 | |
FOREcasT_i1_reg_const =0.01 | |
[inference] | |
ref1len = 127 | |
ref2len = 127 |